DataInn Technologies

Agentic AI

Agents built for the parts of the job that happen after the demo.

Tool-using, multi-agent, and human-gated systems that complete real work inside your systems, with evidence for every step.

What a run looks like

Plan, act, verify, hand off.

This is a real shape of an agent we build: a finance back-office task with three scoped tools, an independent verifier, and a human gate for anything over threshold. The trace on the right is what your reviewers and auditors see.

  • BoundedThe agent can only call the tools it was granted, with the arguments its schema allows.
  • GroundedRetrieval runs with the caller’s permissions. Sources are cited in the output.
  • CheckedA separate verifier step confirms invariants before any write happens.
  • AccountableExceptions go to a named person with the evidence attached.

Capabilities

Build, assure, operate.

Three disciplines, one team. Most agent projects fail in the second and third, so we staff them from the start.

AI Agents service details →LLM Applications & RAG →Case studies →

Build

  • Agent & workflow design
  • Tool and MCP server development
  • Retrieval pipelines with access control
  • Model routing, fallbacks, fine-tuning

Assure

  • Evaluation harnesses & golden sets
  • Red-teaming & prompt-injection defence
  • Policy engines & approval gates
  • Cost and latency budgets per run

Operate

  • Tracing, replay, and alerting
  • Drift and regression monitoring
  • Reviewer tooling & queues
  • Fleet governance across workflows

What we build

Every agent we ship runs the same four-stage loop.

Most agent demos stop at step two. We engineer all four, because production is where the other two stages earn their keep.

Stage 1 of 4

Perceive

Retrieval over your documents, tables, tickets, and APIs with permissions carried through to every lookup. Chunking, embeddings, and hybrid search tuned on your corpus, evaluated on your questions.

What we deliver

  • RAG pipelines with access control
  • Connectors: ERP, CRM, EHR, data warehouse
  • Retrieval evaluation suites

Architectures

Four patterns cover most enterprise work.

We start from the simplest pattern that can do the job, and only add agents when a single one measurably falls short.

Deep dive on agent design
AgentYour data+ citations

Grounded assistant

Knowledge-heavy questions over your own documents and records.

Policy Q&A · clinical reference · contract lookup

Agenterp.querydocs.readmail.sendverify

Tool-using agent

A task that needs to read and write across several systems.

Invoice reconciliation · ticket triage · onboarding

OrchestratorResearcherWriterReviewer

Multi-agent workflow

Work that benefits from specialists: research, draft, review, ship.

Diligence packs · proposal drafting · report generation

AgentExecuteapprove / reject

Human-gated automation

High-stakes decisions where a named person must approve.

Credit decisions · clinical flags · payments over threshold

How we keep agents safe to run

Six commitments in every engagement.

Every action is a typed tool
Agents never get raw database or shell access. Each capability is a schema-checked tool with a permission scope and, where possible, an undo.
Evaluation before launch, regression after
We build the test set with your domain experts first. It runs in CI on every prompt, tool, or model change, so quality cannot silently drift.
Humans at the named moments
Approval gates are part of the design, not a fallback. Reviewers see the agent’s reasoning, evidence, and proposed action in one screen.
Traces you can hand to audit
Every run records inputs, retrieved sources, tool calls, model versions, and decisions. Retention follows your data policy.
Runs where your data lives
Your VPC, your tenancy, or on-prem k3s. Secrets stay out of prompts. Model providers can be swapped without touching the workflow.
Code and knowledge stay with you
Source in your repositories, runbooks in your wiki, and a team that trains yours. No lock-in to us or to a model vendor.

Which pattern fits?

Describe one workflow. We’ll tell you where we’d start.

The same four questions we ask in a scoping call.

  1. 1/4Does the task need to change anything in a system, or only answer questions?

  2. 2/4How many systems does one run touch?

  3. 3/4What happens if the agent gets one case wrong?

  4. 4/4Is the output a decision, or a long document?

Recommended starting point

Answer the four questions

The recommendation appears here. It is a starting point, not a quote; the scoping workshop confirms it against your data.

Start with a conversation

Tell us where your data is letting you down.

A 45-minute readiness call with a senior practitioner. We will tell you plainly what to fix first and what it would take.

Book a readiness call info@datainn.io